Frontend

This post distills the key ideas from the published DEV article “Frontend Performance Optimization: A Comprehensive Guide 🚀” into a concise, production-focused playbook. What matters Fast first paint and interaction: prioritize above-the-fold content, cut JS weight, avoid layout shifts. Ship less: smaller bundles, fewer blocking requests, cache aggressively. Ship smarter: load what’s needed when it’s needed (on demand and in priority order). Core techniques 1) Selective rendering Render only what is visible (e.g., IntersectionObserver + skeletons). Defer heavy components until scrolled into view. 2) Code splitting & dynamic imports Split by route/feature; lazy-load non-critical views. Example (React): const Page = lazy(() => import("./Page")); <Suspense fallback={<div>Loading…</div>}> <Page /> </Suspense>; 3) Prefetching & caching Prefetch likely-next routes/assets (<link rel="prefetch"> or router prefetch). Pre-warm API data with React Query/Next.js loader functions. 4) Priority-based loading Preload critical CSS/hero imagery: <link rel="preload" as="style" href="styles.css">. Use defer/async for non-critical scripts. 5) Compression & transfer Enable Brotli/Gzip at the edge; pre-compress static assets in CI/CD. Serve modern formats (AVIF/WebP) and keep caching headers long-lived. 6) Loading sequence hygiene Order: HTML → critical CSS → critical JS → images/fonts → analytics. Avoid long main-thread tasks; prefer requestIdleCallback for non-urgent work. 7) Tree shaking & dead-code elimination Use ESM named imports; avoid import *. Keep build in production mode with minification + module side-effects flags. Measurement & guardrails Track Core Web Vitals (LCP, INP, CLS) plus TTFB and bundle size per release. Run Lighthouse/WebPageTest in CI; fail builds on regressions above agreed budgets. Monitor real-user metrics (RUM) to validate improvements after deploys. Quick starter checklist Lazy-load routes and heavy widgets. Preload hero font/hero image; inline critical CSS if needed. Turn on Brotli at CDN; cache static assets with versioned filenames. Set performance budgets (JS < 200KB gz, LCP < 2.5s p75, INP < 200ms). Automate audits in CI and watch RUM dashboards after each release. Bottom line: Combine “ship less” (smaller, shaken, compressed bundles) with “ship smarter” (prioritized, on-demand loading) and enforce budgets with automated checks to keep your app fast over time.

This article summarizes the DEV post “How to Use React Query for Efficient Data Fetching” and focuses on a minimal, production-ready setup. Why React Query (TanStack Query) Handles fetching, caching, retries, background refresh, and deduping. Removes useEffect + manual loading/error state boilerplate. Scales to pagination, infinite scroll, and SSR hydration. 3-step setup Install: npm install @tanstack/react-query Create a client and wrap your app: import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; const queryClient = new QueryClient(); const App = () => ( <QueryClientProvider client={queryClient}> <MyComponent /> </QueryClientProvider> ); Fetch with useQuery: import { useQuery } from "@tanstack/react-query"; function UsersList() { const { data, isLoading, error } = useQuery({ queryKey: ["users"], queryFn: () => fetch("/api/users").then((r) => r.json()), }); if (isLoading) return <p>Loading…</p>; if (error) return <p>Something went wrong</p>; return <ul>{data.map((u) => <li key={u.id}>{u.name}</li>)}</ul>; } Features you get “for free” Auto refetch on window focus/network reconnect. Stale-while-revalidate caching with configurable TTLs. Retries with exponential backoff for transient failures. Query invalidation to refresh related data after mutations. Devtools for live inspection of query states. Power tips Prefetch likely-next routes to hide latency (e.g., on hover). Use useInfiniteQuery for endless scroll; surface hasNextPage/fetchNextPage. Pass auth tokens via queryFnContext; centralize fetcher. For SSR/Next.js, hydrate with dehydrate/Hydrate to avoid waterfalls. Performance guardrails Set per-query stale times and retry counts to balance freshness vs. load. Log slow queries and cache hit rate; watch INP/LCP when triggering refetches. Keep query keys stable and descriptive (e.g., ["post", postId]). Bottom line: React Query removes state-management overhead for remote data and delivers faster, more resilient UIs with minimal code.

This summary distills the DEV post “⚡ Vite vs Turbopack — The Present & Future of Frontend Build Tools (2025 Edition)” into key takeaways for teams choosing a tool. Quick comparison Dev speed: Vite is already blazing (ESM + on-demand transforms). Turbopack pushes incremental builds in Rust—slightly better for very large repos. HMR: Vite is instant/reliable; Turbopack is fast and improving. Ecosystem: Vite is framework-agnostic with a large plugin ecosystem; Turbopack is strongest in Next.js today. Prod builds: Vite uses Rollup; Turbopack still leans on Webpack for prod (transitioning). Future: Vite is experimenting with Rolldown (Rust-based Rollup successor) to close the Rust gap. How Vite works (dev vs prod) Dev: native ESM served directly; deps pre-bundled once with esbuild; code transformed on demand. Prod: Rollup bundles with tree shaking, code splitting, and minification. Turbopack highlights Rust core focused on incremental/parallel builds and heavy caching. Today powers Next.js dev mode; production migration is ongoing. When to choose which Pick Vite for framework-agnostic projects, small–medium apps, or when you want the broadest plugin ecosystem and stable DX. Watch Turbopack for large Next.js/monorepo scenarios that will benefit most from incremental builds as it matures. Tips for Vite performance Use explicit imports; avoid barrel files; warm up frequently used files; keep plugin set lean; prefer native tooling (CSS/esbuild/SWC). Bottom line: In 2025 Vite is the safe, fast default for most teams; Turbopack is promising for big Next.js codebases and will get more interesting as Rust-based production builds land.

Based on the DEV article “Building Performant UI with Rust, WebAssembly, and Tailwind CSS,” this summary focuses on the architecture and steps to integrate the stack. Why Rust + WASM Offload CPU-heavy tasks (parsing, transforms, image ops) from the JS main thread. Near-native speed with memory safety. Keeps UI responsive while heavy logic runs in WASM. Why Tailwind here Utility-first styling keeps CSS minimal and predictable. Co-locate styles with components; avoid global collisions. Fast to iterate on responsive layouts for WASM-powered widgets. Integration workflow Compile Rust to WASM with wasm-pack/wasm-bindgen. Import the .wasm module via your bundler (Vite/Webpack/esbuild) and expose JS bindings. Call Rust functions from JS; render results in Tailwind-styled components. Use containers like overflow-x-auto, max-w-full, sm:rounded-lg to keep WASM widgets responsive. Example flow (pseudo) // rust-lib/src/lib.rs (simplified) #[wasm_bindgen] pub fn summarize(data: String) -> String { // heavy work here... format!("size: {}", data.len()) } // web/src/useSummarize.ts import init, { summarize } from "rust-wasm-lib"; export async function useSummarize(input: string) { await init(); return summarize(input); } Performance notes Keep WASM modules small; lazy-load them when the feature is needed. Avoid blocking the main thread—invoke WASM in response to user actions, not eagerly. Profile with browser DevTools + wasm-bindgen debug symbols when needed. Design principles Establish Tailwind design tokens/utilities for spacing/typography early. Encapsulate WASM widgets as reusable components with clear props. Reserve space to prevent layout shifts when results arrive. Good fits Data/analytics dashboards, heavy transforms, visualization prep. Fintech/scientific tools where CPU work dominates. Developer tools needing deterministic, fast processing in-browser. Takeaway: Let Rust/WASM handle the heavy lifting while Tailwind keeps the UI lean and consistent—yielding responsive, performant web experiences.

This note condenses the DEV article “Mastering Frontend Performance: Harnessing the Power of Caching” into actionable steps for modern apps. Why cache Reduce network and CPU cost for repeated data/computation. Improve perceived speed and resilience to flaky networks. Keep UIs responsive under load. Layers to combine HTTP caching: set Cache-Control, ETag, Last-Modified, stale-while-revalidate for API/static responses; prefer immutable, versioned assets. Client memoization: cache expensive computations/render data (useMemo, useCallback, memoized selectors). Data caching: use React Query/SWR/Apollo to dedupe fetches, retry, refetch on focus. Service worker (when appropriate): offline/near-edge caching for shell + static assets. React hook hygiene Memoize derived data: useMemo(() => heavyCompute(input), [input]). Memoize callbacks passed to children to avoid re-renders: useCallback(fn, deps). Keep props stable; avoid recreating objects/functions each render. HTTP cache playbook Static assets: long max-age + immutable on versioned filenames. APIs: choose strategy per route: idempotent reads: max-age/stale-while-revalidate with ETag. personalized or sensitive data: no-store. list endpoints: shorter max-age + revalidation. Prefer CDN edge caching; compress (Brotli) and serve modern formats (AVIF/WebP). UI checks No spinner longer than a couple of seconds; use skeletons and optimistic updates where safe. Avoid layout shift when cached data arrives—reserve space. Track Core Web Vitals (LCP/INP/CLS) and hit-rate for key caches. Quick checklist Versioned static assets + long-lived caching headers. API cache policy per route with ETag/stale-while-revalidate. React memoization for heavy work and stable callbacks. Data-layer cache (React Query/SWR) with sensible stale times + retries. RUM/CI dashboards watching Web Vitals and cache hit rates. Takeaway: Combine HTTP caching, client memoization, and data-layer caches to ship faster pages and keep them fast under real traffic.

This article summarizes the DEV post “Key Performance Metrics for Web Apps and How to Measure Them,” focusing on the most important signals and how to capture them. Core metrics LCP (Largest Contentful Paint): loading speed; target < 2.5s p75. INP/TTI (Interaction to Next Paint / Time to Interactive): interactivity; target INP < 200ms p75. FCP (First Contentful Paint): first visual response. TTFB (Time to First Byte): server responsiveness. Bundle size & request count: total transferred bytes and requests. Measurement toolbox Lighthouse: automated audits; budgets and suggestions. WebPageTest: multi-location runs, filmstrips, waterfalls. RUM (GA or custom): real-user timing for live traffic. React profiler & perf tools: find slow renders/update frequency. Webpack/Vite bundle analyzer: visualize bundle composition and dead weight. Optimization reminders Ship less JS/CSS; enable tree shaking/code splitting. Compress and cache static assets; serve modern image formats. Trim request count; inline critical CSS for hero; defer non-critical JS. Watch layout stability; reserve space to avoid CLS hits. Set and enforce budgets (JS gz < 200KB, LCP < 2.5s p75, INP < 200ms). Takeaway: Track a small, high-signal set of metrics with both lab (Lighthouse/WPT) and field (RUM) data, then enforce budgets so regressions fail fast.